Bibliographies thématiques / 3D-Convolutional Neural Network (3D-CNN)

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Littérature scientifique sur le sujet « 3D-Convolutional Neural Network (3D-CNN) »

Auteur : Grafiati
Publié le 7 juin 2025
Mis à jour le 2 août 2025

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Articles de revues sur le sujet "3D-Convolutional Neural Network (3D-CNN)"

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Chang, Yang-Lang, Tan-Hsu Tan, Wei-Hong Lee, et al. "Consolidated Convolutional Neural Network for Hyperspectral Image Classification." Remote Sensing 14, no. 7 (2022): 1571. http://dx.doi.org/10.3390/rs14071571.

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The performance of hyperspectral image (HSI) classification is highly dependent on spatial and spectral information, and is heavily affected by factors such as data redundancy and insufficient spatial resolution. To overcome these challenges, many convolutional neural networks (CNN) especially 2D-CNN-based methods have been proposed for HSI classification. However, these methods produced insufficient results compared to 3D-CNN-based methods. On the other hand, the high computational complexity of the 3D-CNN-based methods is still a major concern that needs to be addressed. Therefore, this stud
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Lv, Shidong, Tao Long, Zhixian Hou, Liang Yan, and Zhenzhong Li. "3D CNN Hardware Circuit for Motion Recognition Based on FPGA." Journal of Physics: Conference Series 2363, no. 1 (2022): 012030. http://dx.doi.org/10.1088/1742-6596/2363/1/012030.

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In recent years, three-dimensional convolutional neural network (3D CNN) has been widely used in the fields of action recognition and video analysis. The general purpose processors are difficult to achieve efficient and intensive computing, and the deployment of 3D CNN based on FPGA has the advantages of low power consumption, high energy efficiency, and customizability, and has gradually become a hot choice for deploying convolutional neural networks in many embedded scenarios. This paper designs a small 3D convolutional neural network based on the classic 3D convolutional neural network C3D,
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Yin, Junjie, Ningning Huang, Jing Tang, and Meie Fang. "Recognition of 3D Shapes Based on 3V-DepthPano CNN." Mathematical Problems in Engineering 2020 (January 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/7584576.

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This paper proposes a convolutional neural network (CNN) with three branches based on the three-view drawing principle and depth panorama for 3D shape recognition. The three-view drawing principle provides three key views of a 3D shape. A depth panorama contains the complete 2.5D information of each view. 3V-DepthPano CNN is a CNN system with three branches designed for depth panoramas generated from the three key views. This recognition system, i.e., 3V-DepthPano CNN, applies a three-branch convolutional neural network to aggregate the 3D shape depth panorama information into a more compact 3
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SENJAWATI, RINDU TEGAR, ESMERALDA CONTESSA DJAMAL, and FATAN KASYIDI. "Identifikasi Emosi Melalui Sinyal EEG menggunakan 3D-Convolutional Neural Network." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 12, no. 2 (2024): 417. http://dx.doi.org/10.26760/elkomika.v12i2.417.

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ABSTRAKEmosi memberikan peran penting dalam interaksi manusia yang didapat melalui respon yang tepat. Respon yang tak tepat menunjukan adanya gangguan mental sehingga diperlukan identifikasi emosi. Identifikasi dapat dilakukan menggunakan aktivitas sinyal listrik di otak menggunakan Elektroensephalogram (EEG). Karena sinyal EEG pada setiap kanal merupakan urutan data maka dijadikan multi-kanal yang direpresentasikan pada matriks agar urutan-urutan data tetap terjaga. Penggunaan matriks memadukan informasi dari ketiga dimensi (kanal x frekuensi x waktu) dapat menggambarkan kompleksitas dari sin
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Jiang, Haiyang, Yaozong Pan, Jian Zhang, and Haitao Yang. "Battlefield Target Aggregation Behavior Recognition Model Based on Multi-Scale Feature Fusion." Symmetry 11, no. 6 (2019): 761. http://dx.doi.org/10.3390/sym11060761.

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In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the idea of multi-scale feature fusion to the spatio-temporal domain, and enhances the feature extraction ability of the network by combining feature maps of different convolutional layers. In order to reduce the computational complexity of the network, we further improved the multi-fiber network, and finally e
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Dong, Shidu, Zhi Liu, Huaqiu Wang, Yihao Zhang, and Shaoguo Cui. "A Separate 3D Convolutional Neural Network Architecture for 3D Medical Image Semantic Segmentation." Journal of Medical Imaging and Health Informatics 9, no. 8 (2019): 1705–16. http://dx.doi.org/10.1166/jmihi.2019.2797.

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To exploit three-dimensional (3D) context information and improve 3D medical image semantic segmentation, we propose a separate 3D (S3D) convolution neural network (CNN) architecture. First, a two-dimensional (2D) CNN is used to extract the 2D features of each slice in the xy-plane of 3D medical images. Second, one-dimensional (1D) features reassembled from the 2D features in the z-axis are input into a 1D-CNN and are then classified feature-wise. Analysis shows that S3D-CNN has lower time complexity, fewer parameters and less memory space requirements than other 3D-CNNs with a similar structu
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Avula, Sri Lasya. "Efficient 3D Medical Image Segmentation using CoTr: Bridging CNN and Transformer." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4748–54. http://dx.doi.org/10.22214/ijraset.2023.52686.

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Abstract: Neural networks are a subset of machine learning, and they are at the heart of deep learning algorithms. Before CNNs, identifying objects in images was done manually using time-consuming, manual feature extraction methods. The superior performance of convolutional neural networks, when dealing with images, speech, or audio signals sets them apart from other neural networks. Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. Due to the inductive bias of locality and weight sharing inherent in convolutional operations, these
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Hu, Jinlong, Yuezhen Kuang, Bin Liao, Lijie Cao, Shoubin Dong, and Ping Li. "A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification." Computational Intelligence and Neuroscience 2019 (December 31, 2019): 1–9. http://dx.doi.org/10.1155/2019/5065214.

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Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely use
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Chen, Jiangcheng, Sheng Bi, George Zhang, and Guangzhong Cao. "High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network." Sensors 20, no. 4 (2020): 1201. http://dx.doi.org/10.3390/s20041201.

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High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning ho
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Polat, Huseyin, and Homay Danaei Mehr. "Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture." Applied Sciences 9, no. 5 (2019): 940. http://dx.doi.org/10.3390/app9050940.

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Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the pro
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Thèses sur le sujet "3D-Convolutional Neural Network (3D-CNN)"

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Rochford, Matthew. "Visual Speech Recognition Using a 3D Convolutional Neural Network." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2109.

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Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN ar
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Castelli, Filippo Maria. "3D CNN methods in biomedical image segmentation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18796/.

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A definite trend in Biomedical Imaging is the one towards the integration of increasingly complex interpretative layers to the pure data acquisition process. One of the most interesting and looked-forward goals in the field is the automatic segmentation of objects of interest in extensive acquisition data, target that would allow Biomedical Imaging to look beyond its use as a purely assistive tool to become a cornerstone in ambitious large-scale challenges like the extensive quantitative study of the Human Brain. In 2019 Convolutional Neural Networks represent the state of the art in Biomedic
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Liu, Ruixu. "Attention Based Temporal Convolutional Neural Network for Real-time 3D Human Pose Reconstruction." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton157546836015948.

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Broyelle, Antoine. "Automated Pulmonary Nodule Detection on Computed Tomography Images with 3D Deep Convolutional Neural Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231930.

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Object detection on natural images has become a single-stage end-to-end process thanks to recent breakthroughs on deep neural networks. By contrast, automated pulmonary nodule detection is usually a three steps method: lung segmentation, generation of nodule candidates and false positive reduction. This project tackles the nodule detection problem with a single stage modelusing a deep neural network. Pulmonary nodules have unique shapes and characteristics which are not present outside of the lungs. We expect the model to capture these characteristics and to only focus on elements inside the l
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Jackman, Simeon. "Football Shot Detection using Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157438.

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In this thesis, three different neural network architectures are investigated to detect the action of a shot within a football game using video data. The first architecture uses con- ventional convolution and pooling layers as feature extraction. It acts as a baseline and gives insight into the challenges faced during shot detection. The second architecture uses a pre-trained feature extractor. The last architecture uses three-dimensional convolution. All these networks are trained using short video clips extracted from football game video streams. Apart from investigating network architecture
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Pedrazzini, Filippo. "3D Position Estimation using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254876.

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The estimation of the 3D position of an object is one of the most important topics in the computer vision field. Where the final aim is to create automated solutions that can localize and detect objects from images, new high-performing models and algorithms are needed. Due to lack of relevant information in the single 2D images, approximating the 3D position can be considered a complex problem. This thesis describes a method based on two deep learning models: the image net and the temporal net that can tackle this task. The former is a deep convolutional neural network with the intention to ex
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Fucili, Mattia. "3D object detection from point clouds with dense pose voters." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17616/.

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Il riconoscimento di oggetti è sempre stato un compito sfidante per la Computer Vision. Trova applicazione in molti campi, principalmente nell’industria, come ad esempio per permettere ad un robot di trovare gli oggetti da afferrare. Negli ultimi decenni tali compiti hanno trovato nuovi modi di essere raggiunti grazie alla riscoperta delle Reti Neurali, in particolare le Reti Neurali Convoluzionali. Questo tipo di reti ha raggiunto ottimi risultati in molte applicazioni per il riconoscimento e la classificazione degli oggetti. La tendenza, ora, `e quella di utilizzare tali reti anche nell’indust
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Galan, Martínez Silvia 1992. "Chromatin organization : Meta-analysis for the identification and classification of structural patterns." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/670278.

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El desenvolupament de tècniques experimentals basades en la captura de la conformació genòmica (3C), han aportat informació rellevant sobre l’estructura del genoma. En particular el Hi-C, un derivat del 3C, el qual s’ha convertit en una tècnica estàndard per l’estudi de l’estructura 3D del genoma i la seva implicació biològica i funcional. Malgrat tot, existeix una manca de estàndards per el seu anàlisi i interpretació. En aquesta tesi, desenvolupem una xarxa neuronal artificial, Metawaffle, capaç de classificar patrons estructurals sense informació prèvia, que ens permet examinar la capacita
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Li, Vladimir. "Evaluation of the CNN Based Architectures on the Problem of Wide Baseline Stereo Matching." Thesis, KTH, Datorseende och robotik, CVAP, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192476.

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Three-dimensional information is often used in robotics and 3D-mapping. There exist several ways to obtain a three-dimensional map. However, the time of flight used in the laser scanners or the structured light utilized by Kinect-like sensors sometimes are not sufficient. In this thesis, we investigate two CNN based stereo matching methods for obtaining 3D-information from a grayscaled pair of rectified images.While the state-of-the-art stereo matching method utilize a Siamese architecture, in this project a two-channel and a two stream network are trained in an attempt to outperform the state
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Rydén, Anna, and Amanda Martinsson. "Evaluation of 3D motion capture data from a deep neural network combined with a biomechanical model." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176543.

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Motion capture has in recent years grown in interest in many fields from both game industry to sport analysis. The need of reflective markers and expensive multi-camera systems limits the business since they are costly and time-consuming. One solution to this could be a deep neural network trained to extract 3D joint estimations from a 2D video captured with a smartphone. This master thesis project has investigated the accuracy of a trained convolutional neural network, MargiPose, that estimates 25 joint positions in 3D from a 2D video, against a gold standard, multi-camera Vicon-system. The p
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Livres sur le sujet "3D-Convolutional Neural Network (3D-CNN)"

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Kaplan, David L., and Lijie Grace Zhang. Neural Engineering: From Advanced Biomaterials to 3D Fabrication Techniques. Springer, 2018.

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Kaplan, David L., and Lijie Grace Zhang. Neural Engineering: From Advanced Biomaterials to 3D Fabrication Techniques. Springer London, Limited, 2016.

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Kaplan, David L., and Lijie Grace Zhang. Neural Engineering: From Advanced Biomaterials to 3D Fabrication Techniques. Springer, 2016.

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Chapitres de livres sur le sujet "3D-Convolutional Neural Network (3D-CNN)"

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Zhang, Junhui, Li Chen, and Jing Tian. "3D Convolutional Neural Network for Action Recognition." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7299-4_50.

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Kumar, Pramod, Anuj Kumar Singh, Manish Kumar, Sabia, and Shorav Verma. "Facial expression recognition using Two-Stream Convolutional Neural Networks (TSCNNs) and inception 3D Convolutional Neural Network (CNN)." In Computational Methods in Science and Technology. CRC Press, 2024. http://dx.doi.org/10.1201/9781003561651-64.

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Liang, Kaisheng, and Wenlian Lu. "Brain Tumor Segmentation Using 3D Convolutional Neural Network." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46643-5_19.

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Pei, Linmin, Lasitha Vidyaratne, Wei-Wen Hsu, Md Monibor Rahman, and Khan M. Iftekharuddin. "Brain Tumor Classification Using 3D Convolutional Neural Network." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46643-5_33.

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Sharma, Harsh, Arun Kumar Yadav, and Mohit Kumar. "Video Emotion Recognition Using 3D-Convolutional Neural Network." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3358-6_16.

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Lin, Bing-Jhang, Ting-Chen Tsan, Tzu-Chia Tung, You-Hsien Lee, and Chiou-Shann Fuh. "Use 3D Convolutional Neural Network to Inspect Solder Ball Defects." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04167-0_24.

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Channayanamath, Mithun, Akshay Math, Venkat Peddigari, et al. "Dynamic Hand Gesture Recognition Using 3D-Convolutional Neural Network." In Communication Software and Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5397-4_16.

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Roy, Ritaban, Indu Joshi, Abhijit Das, and Antitza Dantcheva. "3D CNN Architectures and Attention Mechanisms for Deepfake Detection." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_10.

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AbstractManipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of state-of-the-art video CNNs including 3D ResNet, 3D ResNeXt, and I3D in detecting manipulated videos. In addition, and toward a more robust det
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Mhathesh, T. S. R., J. Andrew, K. Martin Sagayam, and Lawrence Henesey. "A 3D Convolutional Neural Network for Bacterial Image Classification." In Intelligence in Big Data Technologies—Beyond the Hype. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5285-4_42.

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Zhao, Meng, Chen Jin, Lingmin Jin, Shenghua Teng, and Zuoyong Li. "Cerebral Microbleeds Detection Based on 3D Convolutional Neural Network." In Machine Learning for Cyber Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7_14.

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Actes de conférences sur le sujet "3D-Convolutional Neural Network (3D-CNN)"

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White, Ruth E., Marcello V. Mattei, Benjamin Diaz, et al. "Reconstruction of 3D vascular flow patterns from sparse angiographic data using a 3D convolutional neural network (CNN)." In Clinical and Biomedical Imaging, edited by Barjor S. Gimi and Andrzej Krol. SPIE, 2025. https://doi.org/10.1117/12.3047074.

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Zhao, Wei, Huiyan Qu, Yang Yu, and Chunhua Yin. "3D Scene Collision DetectionTechnology Based on Convolutional Neural Network." In 2024 International Seminar on Artificial Intelligence, Computer Technology and Control Engineering (ACTCE). IEEE, 2024. https://doi.org/10.1109/actce65085.2024.00109.

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Marquez, Alejandra, and Alex Cuadros. "3D Medical Image Segmentation based on 3D Convolutional Neural Networks." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai201812031.

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A neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided. Moreover, a Convolutional Neural Network (CNN) is a type of sophisticated neural network that has shown to efficiently learn tasks related to the area of image analysis (among other areas). One example of these tasks is image segmentation, which aims to find regions or separable objects within an image. A more specific type of segmentation called semantic segmentation, makes sure that each region has a semantic meaning by giving it a l
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Shen, Dan, Carolyn Sheaff, Mengqing Guo, Erik Blasch, Khanh D. Pham, and Genshe Chen. "Three-dimensional convolutional neural network (3D-CNN) for satellite behavior discovery." In Sensors and Systems for Space Applications XIV, edited by Khanh D. Pham and Genshe Chen. SPIE, 2021. http://dx.doi.org/10.1117/12.2589044.

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Liu, Yueqi, Pu Meng, Zhuoyue Diao, Xin Meng, Liqun Zhang, and Xiaodong Li. "Adaptive Weighted 3D Object Image Inference Model Based on Image Complexity." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001832.

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The research on product style classification based on CNN is very active, but the data used to train CNN(Convolutional Neural Networks) are often single-view images of 3D objects, which will lead to the loss of unpredictable object feature information and does not match the real scene. It reduces the quality of the model training. This paper proposes an adaptive weighted CNN model based on image complexity. Feature extraction is performed on images of 3D objects from different perspectives through convolutional neural networks, and the final classification result is obtained by weighting based
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Jatobá, Anthony E. A., Lucas L. Lima, and Marcelo C. Oliveira. "Pulmonary Nodule Classification with 3D Convolutional Neural Networks." In XV Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wvc.2019.7630.

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Lung cancer is a leading cause of death worldwide and its early detection is critical for patient survival. However, the diagnosis is still a challenging task, in which computeraided diagnosis (CADx) systems try to assist by providing a second opinion to a radiologist. In this work, we propose a 3D Convolutional Neural Network for classification of solid pulmonary nodules into benign and malignant. We evaluated different approaches for the nodule volume assembling and tuned our models in an automated fashion. Our models achieved satisfactory results, with AUC of 0.89, accuracy of 81.37% and a s
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Dai, Guoxian, Jin Xie, and Yi Fang. "Siamese CNN-BiLSTM Architecture for 3D Shape Representation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/93.

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Learning a 3D shape representation from a collection of its rendered 2D images has been extensively studied. However, existing view-based techniques have not yet fully exploited the information among all the views of projections. In this paper, by employing recurrent neural network to efficiently capture features across different views, we propose a siamese CNN-BiLSTM network for 3D shape representation learning. The proposed method minimizes a discriminative loss function to learn a deep nonlinear transformation, mapping 3D shapes from the original space into a nonlinear feature space. In the
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Ye, Yuxiang, Bo Li, and Yijuan Lu. "3D sketch-based 3D model retrieval with convolutional neural network." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900083.

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Yamamoto, Shohei, and Tatsuya Harada. "Video Generation Using 3D Convolutional Neural Network." In MM '16: ACM Multimedia Conference. ACM, 2016. http://dx.doi.org/10.1145/2964284.2967287.

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Wang, Chuqi. "A Review on 3D Convolutional Neural Network." In 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2023. http://dx.doi.org/10.1109/icpeca56706.2023.10075760.

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Rapports d'organisations sur le sujet "3D-Convolutional Neural Network (3D-CNN)"

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Cheniour, Amani, Amir Ziabari, Elena Tajuelo Rodriguez, Mohammed Alnaggar, Yann Le Pape, and T. M. Rosseel. Reconstruction of 3D Concrete Microstructures Combining High-Resolution Characterization and Convolutional Neural Network for Image Segmentation. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2311320.

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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MicroBooNE. Reconstructing 3D Charge Depositions in the MicroBooNE Liquid Argon Time Projection Chamber using Convolutional Neural Networks. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/2397314.

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Eka Saputro, Widianto. PENGENALAN ALFABET BAHASA ISYARAT TANGAN PADA CITRA DIGITAL MENGGUNAKAN PENDEKATAN CONVEX HULL DAN CONVOLUTIONAL NEURAL NETWORK (CNN). ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.rwpbjj07.1.

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Eka Saputro, Widianto. PENGENALAN ALFABET BAHASA ISYARAT TANGAN PADA CITRA DIGITAL MENGGUNAKAN PENDEKATAN CONVEX HULL DAN CONVOLUTIONAL NEURAL NETWORK (CNN). ResearchHub Technologies, Inc., 2024. https://doi.org/10.55277/researchhub.rwpbjj07.

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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe
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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental
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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti
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Wells, Daniel, Benjamin Baker, and Kristine Pankow. The Feasibility of Incorporating a 3D Velocity Model Into Earthquake Location Around Salt Lake City, UT Using a Physics Informed Neural Network. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2430497.

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